Emergency Department Utilization Among a Cohort of HIV-Positive Injecting Drug Users in a Canadian Setting
Bibliographic record
Abstract
Background HIV-positive injection drug users (IDU) are known to be at risk for multiple medical problems that may necessitate emergency department (ED) use, however, the relative contribution of HIV disease versus injection-related complications have not been well described. Objectives We examined factors associated with ED use among a prospective cohort of HIV-positive IDU in a Canadian setting. Methods We enrolled HIV-positive IDU into a community-recruited prospective cohort study. We modeled factors associated with the time to first ED visit using Cox regression to determine factors independently associated with ED use. In sub-analyses, we examined ED diagnoses and subsequent hospital admission rates. Results Between December 5, 2005, and April 30, 2008, 428 HIV-positive IDU were enrolled, among whom the cumulative incidence of ED use was 63.7% (95% Confidence Interval [CI]: 59.1% – 68.3%) at 12 months after enrollment. Factors independently associated with time to first ED visit included: unstable housing (Hazard Ratio [HR] = 1.5, 95% CI: 1.1–2.0) and reporting being unable to obtain needed health care services (HR = 2.2, 95% CI: 1.2–4.1), whereas CD4 count and viral load were non-significant. Skin and soft tissue infections (SSTIs) accounted for the greatest proportion of ED visits (17%). Of the 2461 visits to the ED, 419 (17%) were admitted to hospital. Conclusions High rates of ED use were observed among HIV-positive IDU, a behavior that was predicted by unstable housing and limited access to primary care. Factors other than HIV infection appear to be driving ED use among this population in the post-HAART era.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".